9th September 2026 12:20 PM

PhonDAWG - Phonetics Lab Data Analysis Working Group

Our new postdoctoral fellow Dr. Kayla (KyungA) Lee will present her draft poster titled:  L2 Perception and Perceptual Confusions of English Fricatives by EFL Learners: A One-Year Longitudinal Study. 

She will present this poster at the 2026 PSLLT (Pronunciation in Second Language Learning and Teaching) Conference, held at Iowa State University from September 10-12, 2026.

Poster Abstract:    

This study examines how young Korean EFL learners develop L2 perception over one year, in relation to functional load and L1–L2 phonological distance. As Korean and English differ substantially in their phonological inventories, learners may experience persistent difficulty perceiving certain contrasts, which can affect later pronunciation and listening development. Participants were 112 Korean
elementary EFL learners in Grades 4–5 who completed identification tasks at two time points across one year.

The tasks targeted eight English fricatives (/f, θ, s, ʃ, h, v, ð, z/) across multiple vowel contexts using HVPT stimuli (Thomson, 2018). Responses were analyzed using confusion matrices and multidimensional scaling to examine perceptual patterns. Overall, learners showed improved L2 perception over time, with reduced confusion.

This overall improvement was also reflected in a decrease in the mean similarity of confusion (voiceless: 0.417 to 0.314; voiced: 0.483 to 0.414). The largest gains were observed for /s–ʃ/ and /ð–z/, while /f–θ/ remained the most difficult. Notably, high functional load contrasts did not consistently show greater improvement, suggesting that communicative importance alone does not predict acquisition.

These findings highlight the need for pronunciation instruction targeting learner-specific perceptual difficulty and underscore the close relationship between perception and speech development in young learners.

14th September 2026 12:20 PM

Phonetics Lab Meeting

We will discuss this paper by Caleb Belth - "When are Alternations Learned" - abstract below:

Abstract:

Because the acquisition of phonotactics begins before the acquisition of morphology, it is possible that phonotactic knowledge underlies the acquisition of alternations, at least when the two align. An example is final devoicing.

The typical analysis is that alternating obstruents are underlyingly voiced but devoiced in final position, avoiding a phonotactic violation. If phonotactic knowledge underlies alternation learning, acquisition of alternations should follow rapidly after the acquisition of the morphology that produces violations.

Yet different languages with voicing alternations show developmental differences: Dutch-learning children’s developing knowledge is more restricted than that of their German-learning peers.

I hypothesize that children begin attending to alternations, and form non-surface-faithful representations, not as a reflex of phonotactic knowledge, but in response to being unable to morphologically generalize without doing so.

Implemented as an incremental learning model, the proposal explains observed developmental trajectories, with implications for our understanding of alternations and non-surface-faithful representations.

16th September 2026 12:20 PM

Phonetics Lab Meeting

We will discuss this paper   On the relationship between perception and production of L2 sounds:  Evidence from Anglophones' processing of the French /u/-/y/ contrast, Gerda Ana Melnik-Leroy, Rory Turbull, and Sharon Peperkamp. 

Abstract:

Previous studies have yielded contradictory results on the relationship between perception and production in second language (L2) phonological processing.

We re-examine the relationship between the two modalities both within and across processing levels, addressing several issues regarding methodology and statistical analyses. We focus on the perception and production of the French contrast /u/–/y/ by proficient English-speaking late learners of French.

In an experiment with a prelexical perception task (ABX discrimination) and both a prelexical and a lexical production task (pseudoword reading and picture naming), we observe a robust link between perception and production within but not across levels.

Moreover, using a clustering analysis we provide evidence that good perception is a prerequisite for good production.

1st October 2026 04:30 PM

Linguistics Colloquium Speaker: Aditya Vashistha

The Department of Linguistics proudly presents Dr. Aditya Vashistha, assistant professor in the  Cornell Ann S. Bowers College of Computing and Information Science.

Bio:

Dr. Vashistha is an Assistant Professor in the  Cornell Ann S. Bowers College of Computing and Information Science and the leader of  the Cornell Global AI Initiative—an interdisciplinary, university-wide effort to integrate global perspectives into the design, evaluation, and governance of AI technologies. He is also a non-resident fellow at the Center for Democracy & Technology. 

Dr. Vashistha designs, builds, and evaluates Globally Equitable AI technologies that improve socioeconomic outcomes for marginalized communities. His work integrates human-centered methods, computational audits, and field evaluations to ensure that AI systems are safe, inclusive, and grounded in local contexts. ​His research program advances three interconnected thrusts:

1. Information Equity: Understanding and countering harmful content in multilingual and socially stratified communities.
2. Representational Equity: Examining and mitigating cultural, identity, and disability biases in AI through computational audits, cross-cultural studies, and new benchmarks. 
3. Contextual Equity: Designing and evaluating Responsible AI systems for frontline workers in health and education, with deployed systems now supporting thousands of people.

​More broadly, his research lies at the intersection of Human-AI Interaction (HAI), Responsible AI, and ICT for Development (ICTD).

Before Cornell, he completed a Ph.D. in Computer Science and Engineering at the University of Washington, where his dissertation was recognized with the William Chan Memorial Dissertation Award  and the WAGS/ProQuest Innovation in Technology Award.​